Abstract
Multiple types of high throughput genomics data create a potential opportunity to identify driver patterns in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. To identify candidate driver genes and the corresponding driving patterns for resistant and sensitive tumors from the heterogeneous data, we combined gene co-expression modules with mutation modulators and proposed the method to identify driver patterns. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles through weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNV data and somatic mutation data, and a mutation network is constructed from the mutation matrix. Thirdly, candidate modulators are selected from significant genes by clustering vertexs of the mutation network. Finally, a regression tree model is utilized for module network learning, in which the obtained gene modules and candidate modulators are trained for the driving pattern identification and modulators regulatory exploration. Many identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11, and so on.
Highlights
Ovarian cancer is known as a complex genomic disease
The experimental results show that in the driver patterns many of the identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes
We presented a machine-learning approach to integrate somatic mutations, copy number variations (CNVs) and gene expression profiles to distinguish interactions and regulations for dosage-sensitive and dosage-resistant genes of ovarian cancer
Summary
Ovarian cancer is known as a complex genomic disease. During tumorigenesis, many factors contribute to pathological gene expression changes, such as genomic affection and epigenomic affection. The researches have shown that some aberrations are vital for tumorigenesis and most cancers are caused by a small number of driver mutations developed over the course of about two decades[1,2] The detection of these mutations with exceptionally high association between the copy number variations, somatic mutations and gene expression can ascertain disease candidate genes and potential cancer mechanisms. The integration of gene expression, copy number and somatic mutations data to identifying genomics alterations which induce changes in the expression levels of the associated genes, becomes a common task in cancer mechanism and drug response studies. We integrated different level genomics data including gene expression, copy number and somatic mutation to identify drivers of resistance and sensitivity to anti-cancer drugs. The experimental results show that in the driver patterns many of the identified candidate modulators are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes
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